| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Event.sequence | Process.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 1 | A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics | Learning analytics; Learning tactic; Process model; Self-regulated learning | RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Non-srl.indicators.identification | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Non-srl.indicators.identification | other | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Group.comparison | other | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Group.comparison | other | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Group.comparison | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 2 | Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences | Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games | 1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels? | Group.comparison | other | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie |
| 3 | Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data | Human tutoring studies; Learning analtyics; Multimodal | How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process? | Time.to.intervention | other | Multimodal | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Chen, Lujie Karen |
| 3 | Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data | Human tutoring studies; Learning analtyics; Multimodal | How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process? | Time.to.intervention | other | Multimodal | Trace-feedback | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Chen, Lujie Karen |
| 3 | Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data | Human tutoring studies; Learning analtyics; Multimodal | How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process? | Time.to.intervention | other | Multimodal | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Chen, Lujie Karen |
| 4 | Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning | Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) | “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” | Exploring.socio-dynamics | context costumization | Contextual | Trace-other | Group.event.pattern | Content.analysis | Feedback | 2021 | Lee, Alwyn Vwen Yen |
| 4 | Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning | Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) | “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” | Exploring.socio-dynamics | context costumization | Contextual | Trace-other | Group.event.pattern | Content.analysis | Collaboration | 2021 | Lee, Alwyn Vwen Yen |
| 4 | Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning | Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) | “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” | Exploring.socio-dynamics | context costumization | Contextual | Trace-other | Group.event.pattern | Network.analysis | Feedback | 2021 | Lee, Alwyn Vwen Yen |
| 4 | Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning | Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) | “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” | Exploring.socio-dynamics | context costumization | Contextual | Trace-other | Group.event.pattern | Network.analysis | Collaboration | 2021 | Lee, Alwyn Vwen Yen |
| 4 | Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning | Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) | “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” | Exploring.socio-dynamics | context costumization | Contextual | Trace-other | Group.event.pattern | Cluster.analysis | Feedback | 2021 | Lee, Alwyn Vwen Yen |
| 4 | Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning | Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR) | “How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?” | Exploring.socio-dynamics | context costumization | Contextual | Trace-other | Group.event.pattern | Cluster.analysis | Collaboration | 2021 | Lee, Alwyn Vwen Yen |
| 5 | Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms | Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning | RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo |
| 5 | Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms | Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning | RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo |
| 5 | Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms | Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning | RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo |
| 5 | Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms | Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning | RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data? | Method.development | SRL | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo |
| 6 | SAINT+: Integrating Temporal Features for EdNet Correctness Prediction | Deep Learning; Education; Knowledge Tracing; Personalized Learning; Transformer | None | Method.development | None | Lms.log.data | Event | None | Neural.network | Course.design | 2021 | Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck |
| 6 | SAINT+: Integrating Temporal Features for EdNet Correctness Prediction | Deep Learning; Education; Knowledge Tracing; Personalized Learning; Transformer | None | Method.development | None | Lms.log.data | Time | None | Neural.network | Course.design | 2021 | Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck |
| 7 | Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation | Bayesian analysis; Learning; Online instruction | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Course.design | 2021 | Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio |
| 7 | Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation | Bayesian analysis; Learning; Online instruction | None | Method.development | other | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Course.design | 2021 | Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio |
| 7 | Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation | Bayesian analysis; Learning; Online instruction | None | Method.development | other | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Course.design | 2021 | Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio |
| 7 | Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation | Bayesian analysis; Learning; Online instruction | None | Method.development | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Course.design | 2021 | Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio |
| 8 | Temporality revisited: Dynamicity issues in collaborative digital writing research | Collaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge construction | What are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like? | Method.development | collaborative knowledge building | Learning.product | Trace-forum | None | Content.analysis | Collaboration | 2021 | Engerer, Volkmar P. |
| 8 | Temporality revisited: Dynamicity issues in collaborative digital writing research | Collaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge construction | What are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like? | Method.development | collaborative knowledge building | Learning.product | Trace-forum | None | Content.analysis | Learning.indicators | 2021 | Engerer, Volkmar P. |
| 9 | Temporal Cross-Effects in Knowledge Tracing | collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects | we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Process.mining | Time.on.learning | 2021 | Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping |
| 9 | Temporal Cross-Effects in Knowledge Tracing | collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects | we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) | Method.development | knowledge tracing | Performance.measures | Event | Transitional.pattern | Process.mining | Time.on.learning | 2021 | Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping |
| 9 | Temporal Cross-Effects in Knowledge Tracing | collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects | we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) | Method.development | knowledge tracing | Performance.measures | Time | Other.sequential.patterns | Process.mining | Time.on.learning | 2021 | Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping |
| 9 | Temporal Cross-Effects in Knowledge Tracing | collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects | we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) | Method.development | knowledge tracing | Performance.measures | Time | Transitional.pattern | Process.mining | Time.on.learning | 2021 | Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping |
| 9 | Temporal Cross-Effects in Knowledge Tracing | collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects | we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) | Method.development | knowledge tracing | Performance.measures | Trace-quiz | Other.sequential.patterns | Process.mining | Time.on.learning | 2021 | Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping |
| 9 | Temporal Cross-Effects in Knowledge Tracing | collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects | we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects) | Method.development | knowledge tracing | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2021 | Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Collaboration | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-reading | Transitional.pattern | Process.mining | Collaboration | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 10 | Theory-based learning analytics to explore student engagement patterns in a peer review activity | Peer reviews; learning analytics; process mining; student engagement | How can theory-informed LA help identify and interpret engagement patterns in peer reviews? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra |
| 11 | Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy | adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence | RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? | Group.comparison | context costumization | Customized.log.data | Event | Summative | Cluster.analysis | Time.on.learning | 2021 | Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara |
| 11 | Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy | adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence | RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? | Group.comparison | context costumization | Customized.log.data | Time | Summative | Cluster.analysis | Time.on.learning | 2021 | Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara |
| 11 | Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy | adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence | RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? | Group.comparison | context costumization | Performance.measures | Event | Summative | Cluster.analysis | Time.on.learning | 2021 | Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara |
| 11 | Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy | adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence | RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance? | Group.comparison | context costumization | Performance.measures | Time | Summative | Cluster.analysis | Time.on.learning | 2021 | Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Event | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Time | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Time | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Lms.log.data | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Event | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Time | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Time | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | Group.comparison | None | Learning.product | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Event | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Time | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Time | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Lms.log.data | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Event | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Time | Summative | Cluster.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Time | Summative | Process.mining | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 12 | Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics | Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics | RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible? | At-risk.student.identification | None | Learning.product | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2021 | Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-video | Summative | Qualitative.analysis | Course.design | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-video | Summative | Qualitative.analysis | Feedback | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-video | Summative | Visualization.analysis | Course.design | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-video | Summative | Visualization.analysis | Feedback | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-feedback | Summative | Qualitative.analysis | Course.design | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-feedback | Summative | Qualitative.analysis | Feedback | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-feedback | Summative | Visualization.analysis | Course.design | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-feedback | Summative | Visualization.analysis | Feedback | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-other | Summative | Qualitative.analysis | Course.design | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-other | Summative | Qualitative.analysis | Feedback | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-other | Summative | Visualization.analysis | Course.design | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 13 | A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics | Knowledge acquisition; Medical students; Memory | Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance. | Non-srl.indicators.identification | other | Learning.product | Trace-other | Summative | Visualization.analysis | Feedback | 2021 | McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Lms.log.data | Event | Summative | Cluster.analysis | Course.design | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Lms.log.data | Event | Summative | Cluster.analysis | Time.on.learning | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Summative | Cluster.analysis | Course.design | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Summative | Cluster.analysis | Time.on.learning | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Lms.log.data | Time | Summative | Cluster.analysis | Course.design | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Lms.log.data | Time | Summative | Cluster.analysis | Time.on.learning | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Cluster.analysis | Course.design | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Cluster.analysis | Time.on.learning | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Summative | Cluster.analysis | Course.design | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Summative | Cluster.analysis | Time.on.learning | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Self-reported | Time | Summative | Cluster.analysis | Course.design | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 14 | Time-driven modeling of student self-regulated learning in Network analysis-based tutors | Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling | (a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions? | Exploring.srl.processes | SRL | Self-reported | Time | Summative | Cluster.analysis | Time.on.learning | 2021 | Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P. |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Transitional.pattern | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Summative | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Summative | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Event | Summative | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Summative | Basic.statistical.analysis | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Summative | Process.mining | Learning.indicators | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 15 | Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns | Concept mapping;
computer-supported
collaborative learning;
factual knowledge
understanding; conceptual
knowledge understanding;
behavioral pattern | Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception. | Exploring.socio-dynamics | collaborative knowledge building | Self-reported | Trace-forum | Summative | Process.mining | Collaboration | 2021 | Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei |
| 16 | Using process mining for Git log analysis of projects in a software development course | Computer Appl. in Social and Behavioral Sciences; Computers and Education; Education; Educational Technology; Information Systems Applications (incl.Internet); User Interfaces and Human Computer Interaction; general | RQ1: what are the features to extract from the Git log data, and how should be data be processed in order to be usable in the process mining analysis of project development? RQ2: what are the characteristics of the project development process form the perpective of the Git log attributes? RQ3: what are the benefits and limitation of process mining in the Git log analysis of student projects? | Method.development | None | Customized.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Macak, Martin, Kruzelova, Daniela, Chren, Stanislav, Buhnova, Barbora |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Customized.log.data | Event | None | Neural.network | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Customized.log.data | Event | None | Visualization.analysis | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Customized.log.data | Time | None | Neural.network | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Customized.log.data | Time | None | Visualization.analysis | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Learning.product | Event | None | Neural.network | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Learning.product | Event | None | Visualization.analysis | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Learning.product | Time | None | Neural.network | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 17 | Variational Deep Knowledge Tracing for Language Learning | deep learning; knowledge tracing; language learning; student modeling; variational inference | None | Method.development | None | Learning.product | Time | None | Visualization.analysis | Course.design | 2021 | Ruan, Sherry, Wei, Wei, Landay, James |
| 18 | Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant | conversational agent; human-AI interaction; language analysis; online community; online education; theory of mind | RQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA? | Non-srl.indicators.identification | other | Self-reported | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok |
| 18 | Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant | conversational agent; human-AI interaction; language analysis; online community; online education; theory of mind | RQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA? | Method.development | other | Self-reported | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok |
| 19 | Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions | L Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metrics | addressing the problem of inflated values in finding significance in transitions | Method.development | affective learning | Lms.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Matayoshi, Jeffrey, Karumbaiah, Shamya |
| 19 | Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions | L Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metrics | addressing the problem of inflated values in finding significance in transitions | Method.development | affective learning | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2021 | Matayoshi, Jeffrey, Karumbaiah, Shamya |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-feedback | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-feedback | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 20 | Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics | Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies | (1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact? | Exploring.srl.processes | SRL; SSRL | Learning.product | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-other | Transitional.pattern | Basic.statistical.analysis | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-feedback | Transitional.pattern | Basic.statistical.analysis | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Learning.product | Event | Transitional.pattern | Process.mining | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Learning.product | Event | Transitional.pattern | Basic.statistical.analysis | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Learning.product | Trace-other | Transitional.pattern | Process.mining | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Learning.product | Trace-other | Transitional.pattern | Basic.statistical.analysis | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Learning.product | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 21 | Towards the successful game-based learning: Detection and feedback to misconceptions is the key | Elementary education; Games; Teaching/learning strategies | (1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display? | Non-srl.indicators.identification | game-based learning | Learning.product | Trace-feedback | Transitional.pattern | Basic.statistical.analysis | Feedback | 2021 | Yang, Kai-Hsiang, Lu, Bou-Chuan |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 22 | Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development | Log files; Process.mining; Self-regulated learning; TPACK | 1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups? | Exploring.srl.processes | SRL | Learning.product | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Huang, Lingyun, Lajoie, Susanne P |
| 23 | Predicting student success in a blended learning environment | blended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classification | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique |
| 23 | Predicting student success in a blended learning environment | blended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classification | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Method.development | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Event.sequence | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 24 | Supporting actionable intelligence: reframing the analysis of observed study strategies | explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data | RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course? | Group.comparison | cognitive activities (learning actions) | Lms.log.data | Trace-forum | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Event.sequence | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Event.sequence | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Event.sequence | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Event.sequence | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Group.event.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 25 | Analytics of time management and learning strategies for effective online learning in blended environments | blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies | RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data? | Method.development | SRL | Performance.measures | Time | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea |
| 26 | How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content | collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Network.analysis | Collaboration | 2020 | Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf |
| 26 | How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content | collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Network.analysis | Learning.indicators | 2020 | Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf |
| 26 | How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content | collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Network.analysis | Collaboration | 2020 | Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf |
| 26 | How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content | collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Network.analysis | Learning.indicators | 2020 | Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Lms.log.data | Time | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Lms.log.data | Time | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Performance.measures | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Performance.measures | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Performance.measures | Time | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 27 | A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction | None | None | At-risk.student.identification | None | Performance.measures | Time | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Qiao, Chen, Hu, Xiao |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 28 | Process.mining for self-regulated learning assessment in e-learning | e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner | our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-quiz | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 29 | Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data | Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning | 1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies. | Method.development | SRL | Performance.measures | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Visualization.analysis | Time.on.learning | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Learning.product | Trace-feedback | Other.sequential.patterns | Visualization.analysis | Time.on.learning | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Learning.product | Trace-feedback | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Self-reported | Trace-forum | Other.sequential.patterns | Visualization.analysis | Time.on.learning | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Self-reported | Trace-forum | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Self-reported | Trace-feedback | Other.sequential.patterns | Visualization.analysis | Time.on.learning | 2020 | Engerer, Volkmar P. |
| 30 | Implementing dynamicity in research designs for collaborative digital writing | Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design | RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice? | Method.development | collaborative knowledge building | Self-reported | Trace-feedback | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Engerer, Volkmar P. |
| 31 | Predicting Learners' Effortful Behaviour in Adaptive Assessment Using Multimodal Data | adaptive assessment; effort classification; hidden Markov models; multimodal learning analytics | RQ: How can we predict learners’ effort using multimodal data? | Method.development | other | Multimodal | Event | Summative | Cluster.analysis | Time.on.learning | 2020 | Sharma, Kshitij, Papamitsiou, Zacharoula, Olsen, Jennifer K, Giannakos, Michail |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 32 | Analytics of Learning Strategies: Role of Course Design and Delivery Modality | Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning | RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities? | Method.development | SRL | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan |
| 33 | Prediction of students’ early dropout based on their interaction logs in online learning environment | Prediction; extract feature; input-output hidden Markov model; logistic regression; machine learning; online learning environment | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Mubarak, Ahmed A., Cao, Han, Zhang, Weizhen |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 34 | Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity | Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services | 1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes? | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Collaboration | 2020 | Wu, Sheng Yi, Wang, Shu Ming |
| 35 | Reply to which post? An analysis of peer reviews in a high school SPOC | Peer review; SPOC; high school; online interaction; social Network analysis analysis | what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? | Exploring.socio-dynamics | collaborative knowledge building; feedback | Learning.product | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2020 | Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo |
| 35 | Reply to which post? An analysis of peer reviews in a high school SPOC | Peer review; SPOC; high school; online interaction; social Network analysis analysis | what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? | Exploring.socio-dynamics | collaborative knowledge building; feedback | Learning.product | Event | Summative | Basic.statistical.analysis | Feedback | 2020 | Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo |
| 35 | Reply to which post? An analysis of peer reviews in a high school SPOC | Peer review; SPOC; high school; online interaction; social Network analysis analysis | what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? | Exploring.socio-dynamics | collaborative knowledge building; feedback | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | Time.on.learning | 2020 | Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo |
| 35 | Reply to which post? An analysis of peer reviews in a high school SPOC | Peer review; SPOC; high school; online interaction; social Network analysis analysis | what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other? | Exploring.socio-dynamics | collaborative knowledge building; feedback | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | Feedback | 2020 | Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Group.comparison | game-based learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Group.comparison | game-based learning | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Group.comparison | game-based learning | Customized.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Group.comparison | game-based learning | Customized.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 36 | Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game | Board game; Computational participation; Computational thinking; Unplugged | How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies? | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Kuo, Wei Chen, Hsu, Ting Chia |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Event | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Event | Summative | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Trace-other | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Trace-other | Summative | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Event | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Summative | Network.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 37 | Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach | Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics | How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles |
| 38 | Prediction of learners’ dropout in E-learning based on the unusual behaviors | Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors | None | At-risk.student.identification | SRL | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun |
| 38 | Prediction of learners’ dropout in E-learning based on the unusual behaviors | Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors | None | At-risk.student.identification | SRL | Lms.log.data | Trace-reading | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun |
| 38 | Prediction of learners’ dropout in E-learning based on the unusual behaviors | Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors | None | At-risk.student.identification | SRL | Lms.log.data | Trace-quiz | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun |
| 38 | Prediction of learners’ dropout in E-learning based on the unusual behaviors | Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors | None | At-risk.student.identification | SRL | Self-reported | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun |
| 38 | Prediction of learners’ dropout in E-learning based on the unusual behaviors | Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors | None | At-risk.student.identification | SRL | Self-reported | Trace-reading | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun |
| 38 | Prediction of learners’ dropout in E-learning based on the unusual behaviors | Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors | None | At-risk.student.identification | SRL | Self-reported | Trace-quiz | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Summative | Content.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Summative | Content.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Summative | Network.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Summative | Network.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Transitional.pattern | Content.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Transitional.pattern | Content.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Event | Transitional.pattern | Network.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Summative | Content.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Summative | Content.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Summative | Network.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Summative | Network.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Transitional.pattern | Content.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Transitional.pattern | Content.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Transitional.pattern | Network.analysis | Time.on.learning | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 39 | Socio-Temporal Dynamics in Peer Interaction Events | digital peer Network analysiss; relational event modelling; temporality | What are the mechanisms of social interaction in asynchronous online discussions? | Exploring.socio-dynamics | social interactions | Customized.log.data | Trace-forum | Transitional.pattern | Network.analysis | Collaboration | 2020 | Chen, Bodong, Poquet, Oleksandra |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Event | Summative | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-reading | Summative | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-reading | Summative | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-reading | Summative | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-video | Summative | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-video | Summative | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-video | Summative | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-quiz | Summative | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-quiz | Summative | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-quiz | Summative | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Network.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 40 | Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning | epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning | 1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment? | Method.development | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo |
| 41 | Reinforcement Learning for the Adaptive Scheduling of Educational Activities | adaptive learning; online education; reinforcement learning | R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? | Method.development | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C |
| 41 | Reinforcement Learning for the Adaptive Scheduling of Educational Activities | adaptive learning; online education; reinforcement learning | R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? | Method.development | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C |
| 41 | Reinforcement Learning for the Adaptive Scheduling of Educational Activities | adaptive learning; online education; reinforcement learning | R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C |
| 41 | Reinforcement Learning for the Adaptive Scheduling of Educational Activities | adaptive learning; online education; reinforcement learning | R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling? | At-risk.student.identification | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C |
| 42 | Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented reality | Augmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solving | RQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities? | Non-srl.indicators.identification | collaborative knowledge building; other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S. |
| 42 | Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented reality | Augmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solving | RQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities? | Non-srl.indicators.identification | collaborative knowledge building; other | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2020 | Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S. |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Event | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-reading | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Event.sequence | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Frequent.sequence.mining | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 43 | How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement | academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard | RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use? | Exploring.srl.processes | SRL | Performance.measures | Trace-other | Group.event.pattern | Visualization.analysis | Course.design | 2020 | Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Multimodal | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Multimodal | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Multimodal | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Multimodal | Trace-reading | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Multimodal | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Multimodal | Trace-quiz | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Self-reported | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Self-reported | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Self-reported | Trace-reading | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Self-reported | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Exploring.srl.processes | SSRL; collaborative knowledge building | Self-reported | Trace-quiz | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Multimodal | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Multimodal | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Multimodal | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Multimodal | Trace-reading | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Multimodal | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Multimodal | Trace-quiz | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Self-reported | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Self-reported | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Self-reported | Trace-reading | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Self-reported | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 44 | How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? | Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning | (RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions? | Group.comparison | SSRL; collaborative knowledge building | Self-reported | Trace-quiz | Summative | Visualization.analysis | Learning.indicators | 2020 | Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai |
| 45 | Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning | Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning | 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Basic.statistical.analysis | Collaboration | 2020 | Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A. |
| 45 | Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning | Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning | 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A. |
| 45 | Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning | Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning | 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? | Exploring.srl.processes | SRL | Performance.measures | Event | Summative | Basic.statistical.analysis | Collaboration | 2020 | Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A. |
| 45 | Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning | Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning | 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? | Exploring.srl.processes | SRL | Performance.measures | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A. |
| 45 | Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning | Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning | 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? | Exploring.srl.processes | SRL | Multimodal | Event | Summative | Basic.statistical.analysis | Collaboration | 2020 | Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A. |
| 45 | Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning | Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning | 1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success? | Exploring.srl.processes | SRL | Multimodal | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A. |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Time | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-video | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Lms.log.data | Trace-quiz | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Time | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-video | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Summative | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Summative | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Summative | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Transitional.pattern | Process.mining | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 46 | The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes | Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry | 1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions? | Method.development | collaborative knowledge building | Contextual | Trace-quiz | Transitional.pattern | Basic.statistical.analysis | Course.design | 2020 | Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas |
| 47 | Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences | learning analytics; measurement; outlier detection; temporal analysis; time-on-task | This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. | Method.development | other | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2020 | Nguyen, Quan |
| 47 | Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences | learning analytics; measurement; outlier detection; temporal analysis; time-on-task | This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. | Method.development | other | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Nguyen, Quan |
| 47 | Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences | learning analytics; measurement; outlier detection; temporal analysis; time-on-task | This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. | Method.development | other | Lms.log.data | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2020 | Nguyen, Quan |
| 47 | Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences | learning analytics; measurement; outlier detection; temporal analysis; time-on-task | This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance. | Method.development | other | Lms.log.data | Time | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Nguyen, Quan |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Customized.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Customized.log.data | Trace-exercise | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Customized.log.data | Trace-exercise | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Performance.measures | Trace-exercise | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 48 | Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing | deep learning; education; knowledge tracing; personalized learning; transformer | We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations | Method.development | knowledge tracing | Performance.measures | Trace-exercise | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Customized.log.data | Event | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Customized.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Customized.log.data | Trace-exercise | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Customized.log.data | Trace-exercise | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Performance.measures | Trace-exercise | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 49 | RKT: Relation-Aware Self-Attention for Knowledge Tracing | attention Network analysiss; educational data mining; knowledge tracing; relation-aware model | we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student | Method.development | knowledge tracing | Performance.measures | Trace-exercise | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2020 | Pandey, Shalini, Srivastava, Jaideep |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | Method.development | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | Method.development | None | Lms.log.data | Event | Event.sequence | Neural.network | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | Method.development | None | Learner.characteristics | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | Method.development | None | Learner.characteristics | Event | Event.sequence | Neural.network | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | Method.development | None | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | Method.development | None | Performance.measures | Event | Event.sequence | Neural.network | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | At-risk.student.identification | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | At-risk.student.identification | None | Lms.log.data | Event | Event.sequence | Neural.network | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | At-risk.student.identification | None | Learner.characteristics | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | At-risk.student.identification | None | Learner.characteristics | Event | Event.sequence | Neural.network | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | At-risk.student.identification | None | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 50 | Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters | LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining | RQ1. Can we develop useful prediction models for forecasting
students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent
activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance? | At-risk.student.identification | None | Performance.measures | Event | Event.sequence | Neural.network | Learning.indicators | 2020 | Malekian, Donia, Bailey, James, Kennedy, Gregor |
| 51 | In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation | Online discussion modeling; Persuasion; Social media | RQ Feature Is modeling the interplay of comments beneficial
(and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion? | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Other.predictions.models | Collaboration | 2020 | Guo, Zhen, Zhang, Zhe, Singh, Munindar |
| 51 | In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation | Online discussion modeling; Persuasion; Social media | RQ Feature Is modeling the interplay of comments beneficial
(and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion? | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Other.predictions.models | Collaboration | 2020 | Guo, Zhen, Zhang, Zhe, Singh, Munindar |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Other.sequential.patterns | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Other.sequential.patterns | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Performance.measures | Event | Other.sequential.patterns | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Performance.measures | Event | Summative | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Performance.measures | Trace-forum | Other.sequential.patterns | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 52 | High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning | collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily | None | Method.development | collaborative knowledge building | Performance.measures | Trace-forum | Summative | Network.analysis | Time.on.learning | 2020 | Saqr, Mohammed, Nouri, Jalal |
| 53 | Exploring the Affordances of Sequence Mining in Educational Games | Educational games; game-based assessment; learning analytics; sequence mining | To present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene. | Method.development | game-based learning | Customized.log.data | Event | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon |
| 53 | Exploring the Affordances of Sequence Mining in Educational Games | Educational games; game-based assessment; learning analytics; sequence mining | To present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene. | Method.development | game-based learning | Customized.log.data | Trace-other | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2020 | Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Customized.log.data | Event | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Customized.log.data | Trace-feedback | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Customized.log.data | Trace-video | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Customized.log.data | Trace-reading | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Customized.log.data | Trace-exercise | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Learner.characteristics | Event | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Learner.characteristics | Trace-feedback | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Learner.characteristics | Trace-video | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Learner.characteristics | Trace-reading | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Learner.characteristics | Trace-exercise | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 54 | Analyzing Students' Behavior in Blended Learning Environment for Programming Education | Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm | RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance? | Non-srl.indicators.identification | feedback engagement | Learner.characteristics | Trace-other | Summative | Basic.statistical.analysis | Feedback | 2020 | Luo, Jiwen, Wang, Tao |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Event | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Time | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Time | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Time | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Time | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Trace-video | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Trace-video | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Trace-exercise | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Trace-exercise | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Summative | Cluster.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 55 | The importance and meaning of session behaviour in a MOOC | Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC) | RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation? | Exploring.srl.processes | SRL | Self-reported | Trace-other | Summative | Visualization.analysis | Learning.indicators | 2020 | de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Lms.log.data | Trace-exercise | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Self-reported | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Self-reported | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Self-reported | Trace-exercise | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Method.development | SRL | Self-reported | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Lms.log.data | Trace-exercise | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Self-reported | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Self-reported | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Self-reported | Trace-exercise | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | At-risk.student.identification | SRL | Self-reported | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Self-reported | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Self-reported | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Self-reported | Trace-exercise | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 56 | Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs | Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education | RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In | Exploring.srl.processes | SRL | Self-reported | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2020 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos |
| 57 | Temporal analysis of multimodal data to predict collaborative learning outcomes | collaborative learning; learning analytics; multimodal | (RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others? | Method.development | collaborative knowledge building | Multimodal | Event | None | Other.predictions.models | No.learning.focus.outcome | 2020 | Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent |
| 57 | Temporal analysis of multimodal data to predict collaborative learning outcomes | collaborative learning; learning analytics; multimodal | (RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others? | Method.development | collaborative knowledge building | Multimodal | Trace-other | None | Other.predictions.models | No.learning.focus.outcome | 2020 | Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent |
| 58 | Towards Understanding the Lifespan and Spread of Ideas: Epidemiological Modeling of Participation on Twitter | connectivism; engagement patterns; epidemiology; ideas; knowledge creation; Network analysised learning | In this paper, we present preliminary work of tackling this challenge by applying epidemiological modeling to the evolution of ideas. | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2020 | Peri, Sai Santosh Sasank, Chen, Bodong, Dougall, Angela Liegey, Siemens, George |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Event | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Time | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Trace-quiz | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Event | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Event | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Time | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Time | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Time | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Time | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Trace-quiz | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Trace-quiz | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Trace-quiz | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Method.development | other | Performance.measures | Trace-quiz | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Event | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Time | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Trace-quiz | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Trace-quiz | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Event | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Time | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Time | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Time | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Time | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Trace-quiz | Group.event.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Trace-quiz | Group.event.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Trace-quiz | Transitional.pattern | Cluster.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 59 | Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs | association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches | RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance? | Group.comparison | other | Performance.measures | Trace-quiz | Transitional.pattern | Visualization.analysis | Time.on.learning | 2020 | Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki |
| 60 | CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019) | MOOCs; deep learning; dropout prediction; learning analytics | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun |
| 60 | CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019) | MOOCs; deep learning; dropout prediction; learning analytics | None | Method.development | None | Lms.log.data | Time | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun |
| 61 | Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Study | assessment; conceptual change; eye-tracking; visual attention | H1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG. | Non-srl.indicators.identification | other | Multimodal | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Jin, Laipeng, Yu, Dongchuan |
| 61 | Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Study | assessment; conceptual change; eye-tracking; visual attention | H1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG. | Non-srl.indicators.identification | other | Multimodal | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Jin, Laipeng, Yu, Dongchuan |
| 62 | An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout | Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis | None | Method.development | None | Lms.log.data | Event | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi |
| 62 | An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout | Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis | None | Method.development | None | Performance.measures | Event | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi |
| 62 | An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout | Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis | None | At-risk.student.identification | None | Lms.log.data | Event | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi |
| 62 | An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout | Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis | None | At-risk.student.identification | None | Performance.measures | Event | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi |
| 63 | Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks | deep learning; embeddings | None | Method.development | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Kumar, Srijan, Zhang, Xikun, Leskovec, Jure |
| 63 | Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks | deep learning; embeddings | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Kumar, Srijan, Zhang, Xikun, Leskovec, Jure |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Lms.log.data | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Performance.measures | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Performance.measures | Trace-exercise | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Performance.measures | Trace-video | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Performance.measures | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Performance.measures | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Performance.measures | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Self-reported | Trace-video | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 64 | Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load | cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data | O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units. | Non-srl.indicators.identification | other | Self-reported | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-quiz | Event.sequence | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 65 | Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course | Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining | What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos? | Group.comparison | SRL | Lms.log.data | Trace-quiz | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-video | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Method.development | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-feedback | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Event.sequence | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Process.mining | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Cluster.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 66 | Analytics of Learning Strategies: Associations with Academic Performance and Feedback | Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning | Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom? | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Transitional.pattern | Visualization.analysis | Feedback | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-forum | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-forum | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Method.development | SRL | Performance.measures | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-forum | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 67 | Analytics of Learning Strategies: The Association with the Personality Traits | approaches to learning; learning analytics; learning strategies; personality traits | RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted
by learners in a MOOC and any oftheir personality traits? | Exploring.srl.processes | SRL | Performance.measures | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 68 | An application framework for mining online learning processes through event-logs | Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner | RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be? | Method.development | other | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet |
| 69 | Social Network analysising and academic performance: A longitudinal perspective | Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective | to better understand the temporal association between SNS use and academic performance. | Method.development | other | Performance.measures | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul |
| 69 | Social Network analysising and academic performance: A longitudinal perspective | Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective | to better understand the temporal association between SNS use and academic performance. | Method.development | other | Performance.measures | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul |
| 69 | Social Network analysising and academic performance: A longitudinal perspective | Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective | to better understand the temporal association between SNS use and academic performance. | Method.development | other | Self-reported | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul |
| 69 | Social Network analysising and academic performance: A longitudinal perspective | Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective | to better understand the temporal association between SNS use and academic performance. | Method.development | other | Self-reported | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Customized.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Customized.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Customized.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Customized.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Self-reported | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Self-reported | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Self-reported | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 70 | Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World | immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis | None | Group.comparison | game-based learning | Self-reported | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2019 | Reilly, Joseph M, Dede, Chris |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Process.mining | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Process.mining | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Learning.indicators | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 71 | Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning | Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Course.design | 2019 | Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy |
| 72 | Augmenting Knowledge Tracing by Considering Forgetting Behavior | deep neural Network analysis; forgetting behavior; knowledge tracing | We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance. | Method.development | knowledge tracing | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2019 | Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko |
| 72 | Augmenting Knowledge Tracing by Considering Forgetting Behavior | deep neural Network analysis; forgetting behavior; knowledge tracing | We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance. | Method.development | knowledge tracing | Lms.log.data | Time | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2019 | Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko |
| 72 | Augmenting Knowledge Tracing by Considering Forgetting Behavior | deep neural Network analysis; forgetting behavior; knowledge tracing | We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance. | Method.development | knowledge tracing | Lms.log.data | Trace-quiz | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2019 | Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-feedback | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-feedback | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-feedback | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-feedback | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-feedback | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-feedback | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-other | Group.event.pattern | Visualization.analysis | Course.design | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 73 | Data-driven unsupervised Cluster analysis of online learner behaviour | Experimental; Neurosciences; Psychology; Social Sciences; time-series | None | Method.development | other | Performance.measures | Trace-other | Group.event.pattern | Visualization.analysis | Feedback | 2019 | Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Feedback | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Feedback | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 74 | Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools | ITS; exploratory learning; external representation; feedback; maths education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Feedback | 2019 | Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning |
| 75 | Learning anytime, anywhere: a spatio-temporal analysis for online learning | Online course; anytime anywhere; learning performance; spatio-temporal analysis | What are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern? | Group.comparison | None | Multimodal | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long |
| 75 | Learning anytime, anywhere: a spatio-temporal analysis for online learning | Online course; anytime anywhere; learning performance; spatio-temporal analysis | What are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern? | Group.comparison | None | Multimodal | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Learning.product | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Learning.product | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Learning.product | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 76 | Investigating students' interaction patterns and dynamic learning sentiments in online discussions | Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions | (1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions? | Exploring.socio-dynamics | affective learning | Learning.product | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Lms.log.data | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Lms.log.data | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Performance.measures | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Performance.measures | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | Method.development | None | Performance.measures | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Lms.log.data | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Lms.log.data | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Performance.measures | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Performance.measures | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 77 | Transfer Learning Using Representation Learning in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning | (1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability? | At-risk.student.identification | None | Performance.measures | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May |
| 78 | Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills | Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses | None | Method.development | None | Lms.log.data | Time | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Lee, Jinseok, Yeung, Dit-Yan |
| 78 | Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills | Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses | None | Method.development | None | Lms.log.data | Event | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Lee, Jinseok, Yeung, Dit-Yan |
| 78 | Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills | Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses | None | Method.development | None | Performance.measures | Time | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Lee, Jinseok, Yeung, Dit-Yan |
| 78 | Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills | Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses | None | Method.development | None | Performance.measures | Event | None | Other.predictions.models | No.learning.focus.outcome | 2019 | Lee, Jinseok, Yeung, Dit-Yan |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Performance.measures | Event | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Performance.measures | Trace-quiz | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Performance.measures | Trace-reading | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Performance.measures | Trace-exercise | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 79 | The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses | course perfomance; blended learning; temporal pattern | How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement? | Non-srl.indicators.identification | other | Performance.measures | Trace-forum | Summative | Cluster.analysis | Time.on.learning | 2019 | van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen |
| 80 | Mining Activity Log Data to Predict Student's Outcome in a Course | Classification; Education data mining; Learning analytics; prediction | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2019 | Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi |
| 80 | Mining Activity Log Data to Predict Student's Outcome in a Course | Classification; Education data mining; Learning analytics; prediction | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi |
| 80 | Mining Activity Log Data to Predict Student's Outcome in a Course | Classification; Education data mining; Learning analytics; prediction | None | At-risk.student.identification | None | Learner.characteristics | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2019 | Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi |
| 80 | Mining Activity Log Data to Predict Student's Outcome in a Course | Classification; Education data mining; Learning analytics; prediction | None | At-risk.student.identification | None | Learner.characteristics | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi |
| 80 | Mining Activity Log Data to Predict Student's Outcome in a Course | Classification; Education data mining; Learning analytics; prediction | None | At-risk.student.identification | None | Performance.measures | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2019 | Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi |
| 80 | Mining Activity Log Data to Predict Student's Outcome in a Course | Classification; Education data mining; Learning analytics; prediction | None | At-risk.student.identification | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2019 | Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi |
| 81 | Understanding the process of teachers’ technology adoption with a dynamic analytical model | Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research | the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. | Non-srl.indicators.identification | feedback engagement | Lms.log.data | Event | Summative | Basic.statistical.analysis | Feedback | 2019 | Zheng, Longwei, Gibson, David, Gu, Xiaoqing |
| 81 | Understanding the process of teachers’ technology adoption with a dynamic analytical model | Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research | the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. | Non-srl.indicators.identification | feedback engagement | Lms.log.data | Trace-feedback | Summative | Basic.statistical.analysis | Feedback | 2019 | Zheng, Longwei, Gibson, David, Gu, Xiaoqing |
| 81 | Understanding the process of teachers’ technology adoption with a dynamic analytical model | Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research | the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. | Non-srl.indicators.identification | feedback engagement | Lms.log.data | Trace-other | Summative | Basic.statistical.analysis | Feedback | 2019 | Zheng, Longwei, Gibson, David, Gu, Xiaoqing |
| 81 | Understanding the process of teachers’ technology adoption with a dynamic analytical model | Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research | the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. | Method.development | feedback engagement | Lms.log.data | Event | Summative | Basic.statistical.analysis | Feedback | 2019 | Zheng, Longwei, Gibson, David, Gu, Xiaoqing |
| 81 | Understanding the process of teachers’ technology adoption with a dynamic analytical model | Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research | the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. | Method.development | feedback engagement | Lms.log.data | Trace-feedback | Summative | Basic.statistical.analysis | Feedback | 2019 | Zheng, Longwei, Gibson, David, Gu, Xiaoqing |
| 81 | Understanding the process of teachers’ technology adoption with a dynamic analytical model | Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research | the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns. | Method.development | feedback engagement | Lms.log.data | Trace-other | Summative | Basic.statistical.analysis | Feedback | 2019 | Zheng, Longwei, Gibson, David, Gu, Xiaoqing |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-feedback | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 82 | How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study | Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL | RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane |
| 83 | Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses | Physics; eye-tracking; inquiry; learning analytics; simulation | None | Non-srl.indicators.identification | other | Multimodal | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung |
| 83 | Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses | Physics; eye-tracking; inquiry; learning analytics; simulation | None | Non-srl.indicators.identification | other | Multimodal | Time | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung |
| 83 | Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses | Physics; eye-tracking; inquiry; learning analytics; simulation | None | Group.comparison | other | Multimodal | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung |
| 83 | Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses | Physics; eye-tracking; inquiry; learning analytics; simulation | None | Group.comparison | other | Multimodal | Time | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Event | Summative | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Event | Summative | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Trace-other | Summative | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Customized.log.data | Trace-other | Summative | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Event | Summative | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Event | Summative | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Trace-other | Summative | Frequent.sequence.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 84 | Recognizing patterns of student’s modeling behaviour patterns via process mining | Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering | Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions. | Method.development | None | Performance.measures | Trace-other | Summative | Process.mining | Learning.indicators | 2019 | Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Event | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-forum | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-forum | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-forum | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-forum | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-quiz | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-quiz | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-quiz | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-reading | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-reading | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-reading | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-reading | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-other | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-other | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-other | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Multimodal | Trace-other | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-forum | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | Method.development | None | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Event | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-forum | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-forum | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-forum | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-forum | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-quiz | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-quiz | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-quiz | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-reading | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-reading | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-reading | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-reading | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-other | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-other | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-other | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Multimodal | Trace-other | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-forum | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 85 | On multi-device use: Using technological modality profiles to explain differences in students’ learning | Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis | (1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance? | At-risk.student.identification | None | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Sher, Varshita, Hatala, Marek, Gavsevic, Dragan |
| 86 | A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom | Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics | (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing |
| 86 | A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom | Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics | (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? | Non-srl.indicators.identification | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing |
| 86 | A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom | Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics | (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing |
| 86 | A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom | Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics | (1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom? | Group.comparison | other | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing |
| 87 | User behavior pattern detection in unstructured processes – a learning management system case study | Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes | can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns? | Method.development | game-based learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Codish, David, Rabin, Eyal, Ravid, Gilad |
| 87 | User behavior pattern detection in unstructured processes – a learning management system case study | Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes | can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns? | Method.development | game-based learning | Customized.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2019 | Codish, David, Rabin, Eyal, Ravid, Gilad |
| 87 | User behavior pattern detection in unstructured processes – a learning management system case study | Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes | can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns? | Method.development | game-based learning | Customized.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2019 | Codish, David, Rabin, Eyal, Ravid, Gilad |
| 88 | Visual behavior and self-efficacy of game playing: an eye movement analysis | Eye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behavior | this study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying. | Group.comparison | other | Multimodal | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung |
| 88 | Visual behavior and self-efficacy of game playing: an eye movement analysis | Eye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behavior | this study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying. | Group.comparison | other | Self-reported | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Lms.log.data | Event | Summative | Content.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Lms.log.data | Event | Summative | Visualization.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Lms.log.data | Time | Summative | Content.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Lms.log.data | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Lms.log.data | Time | Summative | Visualization.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Learning.product | Event | Summative | Content.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Learning.product | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Learning.product | Event | Summative | Visualization.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Learning.product | Time | Summative | Content.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Learning.product | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 89 | Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums | Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM) | Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations? | Non-srl.indicators.identification | affective learning | Learning.product | Time | Summative | Visualization.analysis | Learning.indicators | 2019 | Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Event | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Time | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Event | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Event | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Time | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Time | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Time | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Performance.measures | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Event | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Event | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Time | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Time | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Time | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 90 | Discovery and temporal analysis of MOOC study patterns | Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis | What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time? | Method.development | None | Learner.characteristics | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2019 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 91 | Reliable Deep Grade Prediction with Uncertainty Estimation | Bayesian Deep Learning; Educational Data Mining; Grade Prediction; Sequential Models; Uncertainty | None | Method.development | None | Performance.measures | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Hu, Qian, Rangwala, Huzefa |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Exploring.srl.processes | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Event.sequence | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Event.sequence | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Time.on.learning | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 92 | Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment | STEM education; Self-regulated learning; Sequential mining; Socially shared regulation | (1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks? | Group.comparison | SSRL; collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2019 | Zheng, Juan, Xing, Wanli, Zhu, Gaoxia |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Transitional.pattern | Process.mining | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Summative | Process.mining | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Summative | Process.mining | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Transitional.pattern | Process.mining | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Summative | Process.mining | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Summative | Process.mining | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Time | Summative | Basic.statistical.analysis | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Transitional.pattern | Basic.statistical.analysis | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Summative | Process.mining | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Summative | Process.mining | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 93 | A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors | Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality | What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities? | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Course.design | 2019 | Cheng, Kun-Hung, Tsai, Chin-Chung |
| 94 | DEBE Feedback for Large Lecture Classroom Analytics | Large lectures; learning analytics; live feedback; mobile application; quantified self | None | Non-srl.indicators.identification | affective learning | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Mitra, Ritayan, Chavan, Pankaj |
| 94 | DEBE Feedback for Large Lecture Classroom Analytics | Large lectures; learning analytics; live feedback; mobile application; quantified self | None | Non-srl.indicators.identification | affective learning | Self-reported | Event | Summative | Basic.statistical.analysis | Feedback | 2019 | Mitra, Ritayan, Chavan, Pankaj |
| 94 | DEBE Feedback for Large Lecture Classroom Analytics | Large lectures; learning analytics; live feedback; mobile application; quantified self | None | Non-srl.indicators.identification | affective learning | Self-reported | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Mitra, Ritayan, Chavan, Pankaj |
| 94 | DEBE Feedback for Large Lecture Classroom Analytics | Large lectures; learning analytics; live feedback; mobile application; quantified self | None | Non-srl.indicators.identification | affective learning | Self-reported | Time | Summative | Basic.statistical.analysis | Feedback | 2019 | Mitra, Ritayan, Chavan, Pankaj |
| 94 | DEBE Feedback for Large Lecture Classroom Analytics | Large lectures; learning analytics; live feedback; mobile application; quantified self | None | Non-srl.indicators.identification | affective learning | Self-reported | Trace-feedback | Summative | Basic.statistical.analysis | Learning.indicators | 2019 | Mitra, Ritayan, Chavan, Pankaj |
| 94 | DEBE Feedback for Large Lecture Classroom Analytics | Large lectures; learning analytics; live feedback; mobile application; quantified self | None | Non-srl.indicators.identification | affective learning | Self-reported | Trace-feedback | Summative | Basic.statistical.analysis | Feedback | 2019 | Mitra, Ritayan, Chavan, Pankaj |
| 95 | Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network | Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences | None | Method.development | knowledge tracing | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka |
| 95 | Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network | Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences | None | Method.development | knowledge tracing | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka |
| 95 | Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network | Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences | None | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka |
| 95 | Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network | Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences | None | Method.development | knowledge tracing | Performance.measures | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Lms.log.data | Time | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Lms.log.data | Time | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Lms.log.data | Trace-video | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Lms.log.data | Trace-video | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Performance.measures | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Performance.measures | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Performance.measures | Time | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Performance.measures | Time | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Performance.measures | Trace-video | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 96 | Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses | Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning | None | Method.development | None | Performance.measures | Trace-video | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2019 | Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen |
| 97 | Knowledge Tracing with Sequential Key-Value Memory Networks | deep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modelling | In this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models. | Method.development | knowledge tracing | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Abdelrahman, Ghodai, Wang, Qing |
| 97 | Knowledge Tracing with Sequential Key-Value Memory Networks | deep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modelling | In this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models. | Method.development | knowledge tracing | Lms.log.data | Trace-quiz | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2019 | Abdelrahman, Ghodai, Wang, Qing |
| 98 | Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context | Data association; fuzzy ontology; fuzzy reasoning; ontology alignment | we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. | Method.development | None | Multimodal | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Wang, Wei, Mu, Wenxin, Gou, Juanqiong |
| 98 | Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context | Data association; fuzzy ontology; fuzzy reasoning; ontology alignment | we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. | Method.development | None | Multimodal | Event | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Wang, Wei, Mu, Wenxin, Gou, Juanqiong |
| 98 | Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context | Data association; fuzzy ontology; fuzzy reasoning; ontology alignment | we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. | Method.development | None | Multimodal | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Wang, Wei, Mu, Wenxin, Gou, Juanqiong |
| 98 | Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context | Data association; fuzzy ontology; fuzzy reasoning; ontology alignment | we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved. | Method.development | None | Multimodal | Time | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2019 | Wang, Wei, Mu, Wenxin, Gou, Juanqiong |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Lms.log.data | Trace-quiz | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Performance.measures | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Performance.measures | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | Method.development | None | Performance.measures | Trace-quiz | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Lms.log.data | Trace-quiz | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Performance.measures | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Performance.measures | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 99 | Analysing the predictive power for anticipating assignment grades in a massive open online course | None | None | At-risk.student.identification | None | Performance.measures | Trace-quiz | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos |
| 100 | Investigating temporal access in a flipped classroom: procrastination persists | Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law | What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance? Are there any significant differences among student performance groups with respect to LMS interaction? | Group.comparison | None | Lms.log.data | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed |
| 100 | Investigating temporal access in a flipped classroom: procrastination persists | Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law | What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance? Are there any significant differences among student performance groups with respect to LMS interaction? | Group.comparison | None | Lms.log.data | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed |
| 100 | Investigating temporal access in a flipped classroom: procrastination persists | Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law | What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance? Are there any significant differences among student performance groups with respect to LMS interaction? | Group.comparison | None | Performance.measures | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed |
| 100 | Investigating temporal access in a flipped classroom: procrastination persists | Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law | What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance? Are there any significant differences among student performance groups with respect to LMS interaction? | Group.comparison | None | Performance.measures | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed |
| 101 | What’s Next? A Recommendation System for Industrial Training | Chemistry and Earth Sciences; Computer Science; Industrial trainin; Physics; Statistics for Engineering | None | Method.development | None | Learner.characteristics | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Srivastava, Rajiv, Palshikar, Girish Keshav, Chaurasia, Saheb, Dixit, Arati |
| 102 | Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning | efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining | None | Exploring.srl.processes | srl; affective learning; game-based learning | Customized.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2018 | Taub, Michelle, Azevedo, Roger |
| 102 | Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning | efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining | None | Exploring.srl.processes | srl; affective learning; game-based learning | Customized.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2018 | Taub, Michelle, Azevedo, Roger |
| 102 | Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning | efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining | None | Exploring.srl.processes | srl; affective learning; game-based learning | Self-reported | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2018 | Taub, Michelle, Azevedo, Roger |
| 102 | Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning | efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining | None | Exploring.srl.processes | srl; affective learning; game-based learning | Self-reported | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2018 | Taub, Michelle, Azevedo, Roger |
| 102 | Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning | efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining | None | Exploring.srl.processes | srl; affective learning; game-based learning | Multimodal | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2018 | Taub, Michelle, Azevedo, Roger |
| 102 | Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning | efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining | None | Exploring.srl.processes | srl; affective learning; game-based learning | Multimodal | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2018 | Taub, Michelle, Azevedo, Roger |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Event | Other.sequential.patterns | Network.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Event | Other.sequential.patterns | Content.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Event | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Trace-forum | Other.sequential.patterns | Network.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Trace-forum | Other.sequential.patterns | Content.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Trace-forum | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Network.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Content.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Network.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Content.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 103 | A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality | collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2018 | Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru |
| 104 | A Sequence Data Model for Analyzing Temporal Patterns of Student Data | Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2018 | Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon |
| 104 | A Sequence Data Model for Analyzing Temporal Patterns of Student Data | Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling | None | Method.development | None | Lms.log.data | Time | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2018 | Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon |
| 104 | A Sequence Data Model for Analyzing Temporal Patterns of Student Data | Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling | None | Method.development | None | Performance.measures | Event | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2018 | Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon |
| 104 | A Sequence Data Model for Analyzing Temporal Patterns of Student Data | Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling | None | Method.development | None | Performance.measures | Time | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2018 | Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon |
| 104 | A Sequence Data Model for Analyzing Temporal Patterns of Student Data | Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling | None | Method.development | None | Learner.characteristics | Event | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2018 | Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon |
| 104 | A Sequence Data Model for Analyzing Temporal Patterns of Student Data | Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling | None | Method.development | None | Learner.characteristics | Time | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2018 | Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon |
| 105 | Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics | dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension | None | Method.development | other | Learning.product | Trace-reading | Summative | Neural.network | Learning.indicators | 2018 | Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S |
| 105 | Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics | dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension | None | Method.development | other | Learning.product | Trace-reading | Summative | Content.analysis | Learning.indicators | 2018 | Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S |
| 105 | Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics | dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension | None | Method.development | other | Learning.product | Trace-quiz | Summative | Neural.network | Learning.indicators | 2018 | Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S |
| 105 | Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics | dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension | None | Method.development | other | Learning.product | Trace-quiz | Summative | Content.analysis | Learning.indicators | 2018 | Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Event | Summative | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-reading | Summative | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-quiz | Summative | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Customized.log.data | Trace-quiz | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Event | Summative | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-reading | Summative | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-reading | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-reading | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-quiz | Summative | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-quiz | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 106 | Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns | Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis | None | Non-srl.indicators.identification | motivation | Self-reported | Trace-quiz | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2018 | Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien |
| 107 | Understanding user behavioral patterns in open knowledge communities | Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis | None | Non-srl.indicators.identification | collaborative knowledge building | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan |
| 107 | Understanding user behavioral patterns in open knowledge communities | Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis | None | Non-srl.indicators.identification | collaborative knowledge building | Customized.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan |
| 107 | Understanding user behavioral patterns in open knowledge communities | Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis | None | Non-srl.indicators.identification | collaborative knowledge building | Customized.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan |
| 107 | Understanding user behavioral patterns in open knowledge communities | Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis | None | Non-srl.indicators.identification | collaborative knowledge building | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan |
| 108 | Predicting Learning Difficulty Based on Gaze and Pupil Response | e-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysis | None | Method.development | other | Multimodal | Time | None | Other.predictions.models | Time.on.learning | 2018 | Parikh, Saurin, Kalva, Hari |
| 108 | Predicting Learning Difficulty Based on Gaze and Pupil Response | e-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysis | None | Method.development | other | Multimodal | Trace-reading | None | Other.predictions.models | Time.on.learning | 2018 | Parikh, Saurin, Kalva, Hari |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Customized.log.data | Trace-reading | Other.sequential.patterns | Other.predictions.models | Course.design | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Customized.log.data | Trace-reading | Other.sequential.patterns | Other.predictions.models | Feedback | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Customized.log.data | Trace-other | Other.sequential.patterns | Other.predictions.models | Course.design | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Customized.log.data | Trace-other | Other.sequential.patterns | Other.predictions.models | Feedback | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Learning.product | Trace-reading | Other.sequential.patterns | Other.predictions.models | Course.design | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Learning.product | Trace-reading | Other.sequential.patterns | Other.predictions.models | Feedback | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Learning.product | Trace-other | Other.sequential.patterns | Other.predictions.models | Course.design | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 109 | Social tagging strategy for enhancing e-learning experience | Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies | None | Method.development | collaborative knowledge building | Learning.product | Trace-other | Other.sequential.patterns | Other.predictions.models | Feedback | 2018 | Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana |
| 110 | How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and Spatialities | FLOSS; learning across scales; situated cognition | None | Method.development | collaborative knowledge building | Contextual | Time | None | Qualitative.analysis | Course.design | 2018 | Johri, Aditya |
| 110 | How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and Spatialities | FLOSS; learning across scales; situated cognition | None | Method.development | collaborative knowledge building | Contextual | Time | None | Qualitative.analysis | Time.on.learning | 2018 | Johri, Aditya |
| 111 | Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses | Timing; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effect | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W. |
| 111 | Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses | Timing; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effect | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2018 | Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W. |
| 112 | Observational Scaffolding for Learning Analytics: A Methodological Proposal | Lag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analytics | None | Method.development | collaborative knowledge building | Customized.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara |
| 112 | Observational Scaffolding for Learning Analytics: A Methodological Proposal | Lag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analytics | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Event | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Time | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Time | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Trace-reading | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Trace-reading | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Trace-other | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 113 | Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours | Learning analytics; engagement; learning design; temporal analysis; time management | None | Non-srl.indicators.identification | time management | Lms.log.data | Trace-other | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 114 | Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning | Academic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; Students | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H |
| 114 | Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning | Academic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; Students | None | At-risk.student.identification | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Summative | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Summative | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Event | Transitional.pattern | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Event | Summative | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Trace-forum | Transitional.pattern | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Trace-forum | Summative | Process.mining | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 115 | Effects of success v failure cases on learner-learner interaction | Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning | None | Group.comparison | other | Lms.log.data | Trace-forum | Summative | Content.analysis | Learning.indicators | 2018 | Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Summative | Visualization.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Event | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Event | Summative | Visualization.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Time | Summative | Basic.statistical.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Time | Summative | Visualization.analysis | Course.design | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 116 | Linking Students' Timing of Engagement to Learning Design and Academic Performance | engagement; higher education; learning analytics; learning design; temporal; virtual learning environment | None | Group.comparison | time on task | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2018 | Nguyen, Quan, Huptych, Michal, Rienties, Bart |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Event | Summative | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Time | Summative | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Time | Summative | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | Group.comparison | None | Lms.log.data | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Event | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Time | Transitional.pattern | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Process.mining | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Cluster.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 117 | Temporal Dynamics of MOOC Learning Trajectories | MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2018 | Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina |
| 118 | A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems | Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning | None | Method.development | None | Customized.log.data | Event | None | Basic.statistical.analysis | Course.design | 2018 | Liu, Ran, Stamper, John C, Davenport, Jodi |
| 118 | A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems | Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning | None | Method.development | None | Customized.log.data | Trace-video | None | Basic.statistical.analysis | Course.design | 2018 | Liu, Ran, Stamper, John C, Davenport, Jodi |
| 118 | A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems | Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning | None | Method.development | None | Customized.log.data | Trace-other | None | Basic.statistical.analysis | Course.design | 2018 | Liu, Ran, Stamper, John C, Davenport, Jodi |
| 118 | A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems | Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning | None | Method.development | None | Multimodal | Event | None | Basic.statistical.analysis | Course.design | 2018 | Liu, Ran, Stamper, John C, Davenport, Jodi |
| 118 | A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems | Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning | None | Method.development | None | Multimodal | Trace-video | None | Basic.statistical.analysis | Course.design | 2018 | Liu, Ran, Stamper, John C, Davenport, Jodi |
| 118 | A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems | Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning | None | Method.development | None | Multimodal | Trace-other | None | Basic.statistical.analysis | Course.design | 2018 | Liu, Ran, Stamper, John C, Davenport, Jodi |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-other | Transitional.pattern | Frequent.sequence.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 119 | Behavioral patterns of knowledge construction in online cooperative translation activities | Behavioral pattern; Cooperative translation; Engagement; Knowledge construction | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2018 | Yang, Xianmin, Li, Jihong, Xing, Beibei |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Group.event.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Transitional.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Group.event.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Transitional.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Trace-video | Group.event.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Non-srl.indicators.identification | time on task | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Event | Group.event.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Event | Transitional.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Time | Group.event.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Time | Transitional.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Trace-video | Group.event.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 120 | Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences | EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis | None | Method.development | time on task | Lms.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Feedback | 2018 | Boroujeni, Mina Shirvani, Dillenbourg, Pierre |
| 121 | Video-Based Question Generation for Mobile Learning | Mobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video Fragment | None | Method.development | None | Customized.log.data | Event | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2018 | Nimkanjana, Klinsukon, Witosurapot, Suntorn |
| 121 | Video-Based Question Generation for Mobile Learning | Mobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video Fragment | None | Method.development | None | Customized.log.data | Trace-video | Other.sequential.patterns | Basic.statistical.analysis | No.learning.focus.outcome | 2018 | Nimkanjana, Klinsukon, Witosurapot, Suntorn |
| 122 | Representing and Predicting Student Navigational Pathways in Online College Courses | long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2018 | Yu, Renzhe, Jiang, Daokun, Warschauer, Mark |
| 122 | Representing and Predicting Student Navigational Pathways in Online College Courses | long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2018 | Yu, Renzhe, Jiang, Daokun, Warschauer, Mark |
| 122 | Representing and Predicting Student Navigational Pathways in Online College Courses | long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling | None | At-risk.student.identification | None | Lms.log.data | Event | Other.sequential.patterns | Neural.network | No.learning.focus.outcome | 2018 | Yu, Renzhe, Jiang, Daokun, Warschauer, Mark |
| 122 | Representing and Predicting Student Navigational Pathways in Online College Courses | long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling | None | At-risk.student.identification | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2018 | Yu, Renzhe, Jiang, Daokun, Warschauer, Mark |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Event | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Event | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Trace-forum | Summative | Process.mining | Learning.indicators | 2018 | Bakla, Arif |
| 123 | Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study | Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation | None | Group.comparison | collaborative knowledge building | Learning.product | Trace-forum | Summative | Process.mining | Collaboration | 2018 | Bakla, Arif |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Other.sequential.patterns | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Other.sequential.patterns | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Learning.product | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 124 | Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions | Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis | None | Group.comparison | collaborative knowledge building | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | Learning.indicators | 2018 | Chiu, MIng Ming |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Event | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-quiz | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-quiz | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-reading | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-reading | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-exercise | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-exercise | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-forum | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Method.development | None | Customized.log.data | Trace-forum | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Event | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-quiz | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-quiz | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-reading | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-reading | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-exercise | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-exercise | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-forum | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 125 | An empirical study of using sequential behavior pattern mining approach to predict learning styles | Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining | None | Non-srl.indicators.identification | None | Customized.log.data | Trace-forum | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2018 | Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi |
| 126 | Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics | Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie |
| 126 | Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics | Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie |
| 126 | Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics | Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education | None | At-risk.student.identification | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie |
| 126 | Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics | Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education | None | At-risk.student.identification | None | Performance.measures | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie |
| 126 | Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics | Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education | None | At-risk.student.identification | None | Learner.characteristics | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie |
| 126 | Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics | Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education | None | At-risk.student.identification | None | Learner.characteristics | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2018 | Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Lms.log.data | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Lms.log.data | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Lms.log.data | Trace-quiz | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Lms.log.data | Trace-reading | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Performance.measures | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Performance.measures | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Performance.measures | Trace-quiz | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Performance.measures | Trace-reading | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Learner.characteristics | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Learner.characteristics | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Learner.characteristics | Trace-quiz | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 127 | Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success | higher education; learning analytics; predictive modeling; self-regulated learning; student engagement | None | At-risk.student.identification | srl | Learner.characteristics | Trace-reading | Summative | Basic.statistical.analysis | Time.on.learning | 2017 | Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Customized.log.data | Time | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Customized.log.data | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Performance.measures | Event | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Performance.measures | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Performance.measures | Time | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Performance.measures | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Self-reported | Event | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Self-reported | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Self-reported | Time | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Time.to.intervention | srl | Self-reported | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Customized.log.data | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Customized.log.data | Time | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Customized.log.data | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Performance.measures | Event | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Performance.measures | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Performance.measures | Time | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Performance.measures | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Self-reported | Event | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Self-reported | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Self-reported | Time | Other.sequential.patterns | Other.predictions.models | Time.on.learning | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 128 | Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems | diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning | None | Method.development | srl | Self-reported | Time | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Self-reported | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Self-reported | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Learner.characteristics | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Learner.characteristics | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Performance.measures | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 129 | Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study | Adult learning; Distance education and telelearning; Lifelong learning | None | Non-srl.indicators.identification | other | Performance.measures | Time | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | Method.development | None | Performance.measures | Event | None | Other.predictions.models | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | Method.development | None | Performance.measures | Event | None | Cluster.analysis | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | Method.development | None | Performance.measures | Event | None | Visualization.analysis | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | At-risk.student.identification | None | Performance.measures | Event | None | Other.predictions.models | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | At-risk.student.identification | None | Performance.measures | Event | None | Cluster.analysis | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | At-risk.student.identification | None | Performance.measures | Event | None | Visualization.analysis | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | Group.comparison | None | Performance.measures | Event | None | Other.predictions.models | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | Group.comparison | None | Performance.measures | Event | None | Cluster.analysis | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 130 | Analyzing undergraduate students' performance using educational data mining | Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes | None | Group.comparison | None | Performance.measures | Event | None | Visualization.analysis | No.learning.focus.outcome | 2017 | Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Event | Other.sequential.patterns | Network.analysis | Learning.indicators | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Event | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Event | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Event | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Trace-other | Other.sequential.patterns | Network.analysis | Learning.indicators | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Trace-other | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Trace-other | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Trace-other | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Time | Other.sequential.patterns | Network.analysis | Learning.indicators | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Time | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Time | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 131 | Gaining Insight by Transforming Between Temporal Representations of Human Interaction | Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses | None | Non-srl.indicators.identification | affective learning; collaborative knowledge building | Contextual | Time | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Event | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Event | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Event | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Event | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-other | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-other | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-video | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-video | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-video | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-video | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-video | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-video | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-forum | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-forum | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Time | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Time | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Time | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Time | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Time | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Customized.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Event | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Event | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Event | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Event | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Event | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-other | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-other | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-other | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-other | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-video | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-video | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-video | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-video | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-video | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-video | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-forum | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-forum | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-forum | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Time | Transitional.pattern | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Time | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Time | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Time | Summative | Process.mining | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Time | Summative | Basic.statistical.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 132 | Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions | Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data | None | Exploring.srl.processes | srl; collaborative knowledge building; affective learning | Learning.product | Time | Summative | Visualization.analysis | Collaboration | 2017 | Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Event | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-exercise | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-exercise | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-exercise | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-reading | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-reading | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Customized.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-exercise | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-exercise | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-exercise | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-reading | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-reading | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 133 | Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs | SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis | None | Group.comparison | other | Performance.measures | Trace-other | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Event.sequence | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Event.sequence | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Event | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Event | Event.sequence | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Event.sequence | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Event.sequence | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Frequent.sequence.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 134 | Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse | Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Time.on.learning | 2017 | Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Event | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Event | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Event | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Event | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Time | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Time | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Time | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Time | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Trace-other | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Trace-other | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Trace-other | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Lms.log.data | Trace-other | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Event | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Event | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Event | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Event | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Event | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Time | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Time | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Time | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Time | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Time | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Trace-other | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Trace-other | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Trace-other | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Method.development | other | Performance.measures | Trace-other | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Event | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Event | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Event | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Event | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Time | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Time | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Time | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Time | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Trace-other | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Trace-other | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Trace-other | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Lms.log.data | Trace-other | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Event | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Event | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Event | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Event | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Event | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Time | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Time | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Time | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Time | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Time | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Trace-other | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Trace-other | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Trace-other | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | At-risk.student.identification | other | Performance.measures | Trace-other | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Event | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Event | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Event | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Event | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Time | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Time | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Time | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Time | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Trace-other | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Trace-other | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Trace-other | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Lms.log.data | Trace-other | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Event | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Event | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Event | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Event | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Event | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Time | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Time | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Time | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Time | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Time | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Trace-other | Summative | Visualization.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Trace-other | Summative | Network.analysis | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Trace-other | Summative | Other.predictions.models | Time.on.learning | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 135 | An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments | dashboards; introductory programming; learning analytics; machine learning; peer tutors | None | Time.to.intervention | other | Performance.measures | Trace-other | Summative | Other.predictions.models | Collaboration | 2017 | Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi |
| 136 | Using Programming Process Data to Detect Differences in Students' Patterns of Programming | educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model | None | Method.development | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Carter, Adam Scott, Hundhausen, Christopher David |
| 136 | Using Programming Process Data to Detect Differences in Students' Patterns of Programming | educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model | None | Method.development | None | Lms.log.data | Event | Summative | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Carter, Adam Scott, Hundhausen, Christopher David |
| 136 | Using Programming Process Data to Detect Differences in Students' Patterns of Programming | educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model | None | Method.development | None | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Carter, Adam Scott, Hundhausen, Christopher David |
| 136 | Using Programming Process Data to Detect Differences in Students' Patterns of Programming | educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model | None | Method.development | None | Performance.measures | Event | Summative | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Carter, Adam Scott, Hundhausen, Christopher David |
| 137 | A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies | Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics | None | Method.development | other | Multimodal | Event | Transitional.pattern | Process.mining | Time.on.learning | 2017 | Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V. |
| 137 | A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies | Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics | None | Method.development | other | Multimodal | Event | Transitional.pattern | Visualization.analysis | Time.on.learning | 2017 | Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V. |
| 137 | A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies | Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics | None | Method.development | other | Multimodal | Trace-other | Transitional.pattern | Process.mining | Time.on.learning | 2017 | Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V. |
| 137 | A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies | Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics | None | Method.development | other | Multimodal | Trace-other | Transitional.pattern | Visualization.analysis | Time.on.learning | 2017 | Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V. |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Method.development | srl | Performance.measures | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Lms.log.data | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-reading | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-reading | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-quiz | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-quiz | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-video | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-video | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-other | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-other | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-other | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 138 | Learning analytics to unveil learning strategies in a flipped classroom | learning strategies; sequence analysis; self-regulated learning; learning analytics | None | Exploring.srl.processes | srl | Performance.measures | Trace-other | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin |
| 139 | Shapes of Educational Data in an Online Calculus Course | Markov chain; clickstream; sequence analysis | None | Method.development | None | Lms.log.data | Event | Event.sequence | Other.predictions.models | Learning.indicators | 2017 | Caprotti, Olga |
| 139 | Shapes of Educational Data in an Online Calculus Course | Markov chain; clickstream; sequence analysis | None | Method.development | None | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2017 | Caprotti, Olga |
| 139 | Shapes of Educational Data in an Online Calculus Course | Markov chain; clickstream; sequence analysis | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | Learning.indicators | 2017 | Caprotti, Olga |
| 139 | Shapes of Educational Data in an Online Calculus Course | Markov chain; clickstream; sequence analysis | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | Learning.indicators | 2017 | Caprotti, Olga |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Content.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 140 | Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis | Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Group.event.pattern | Visualization.analysis | Learning.indicators | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-reading | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Event | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-exercise | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-reading | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-video | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-video | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Non-srl.indicators.identification | other | Self-reported | Trace-quiz | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Event | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-exercise | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-exercise | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-reading | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-reading | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-video | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-video | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-quiz | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Lms.log.data | Trace-quiz | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Event | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Event | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-exercise | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-exercise | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-exercise | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-exercise | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-reading | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-reading | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-reading | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-reading | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-reading | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-reading | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-video | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-video | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-video | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-video | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-quiz | Summative | Frequent.sequence.mining | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 141 | Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance | Learning analytics; approaches to learning; learning strategy; self-reported measures | None | Group.comparison | other | Self-reported | Trace-quiz | Summative | Cluster.analysis | Learning.indicators | 2017 | Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Summative | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Summative | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Summative | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.socio-dynamics | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Summative | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Summative | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Summative | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Qualitative.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Qualitative.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Basic.statistical.analysis | Collaboration | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 142 | Co-regulation and knowledge construction in an online synchronous problem based learning setting | Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning | None | Exploring.srl.processes | srl; collaborative knowledge building | Contextual | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2017 | Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Event | Summative | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Event | Summative | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Event | Group.event.pattern | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Event | Group.event.pattern | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Trace-forum | Summative | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Trace-forum | Summative | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Trace-forum | Group.event.pattern | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Exploring.socio-dynamics | other | Learning.product | Trace-forum | Group.event.pattern | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Event | Summative | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Event | Summative | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Event | Group.event.pattern | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Event | Group.event.pattern | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Trace-forum | Summative | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Trace-forum | Summative | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Trace-forum | Group.event.pattern | Frequent.sequence.mining | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 143 | Role Modelling in MOOC Discussion Forums | Discussion Forums; Social Network Analysis; Temporal data | None | Non-srl.indicators.identification | other | Learning.product | Trace-forum | Group.event.pattern | Cluster.analysis | Course.design | 2017 | Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich |
| 144 | Mining frequent learning pathways from a large educational dataset | Graph mining; Learning pathways; Process.mining; Sequence mining | None | Method.development | None | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | No.learning.focus.outcome | 2017 | Patel, Nirmal, Sellman, Collin, Lomas, Derek |
| 144 | Mining frequent learning pathways from a large educational dataset | Graph mining; Learning pathways; Process.mining; Sequence mining | None | Method.development | None | Lms.log.data | Event | Group.event.pattern | Process.mining | No.learning.focus.outcome | 2017 | Patel, Nirmal, Sellman, Collin, Lomas, Derek |
| 144 | Mining frequent learning pathways from a large educational dataset | Graph mining; Learning pathways; Process.mining; Sequence mining | None | Method.development | None | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | No.learning.focus.outcome | 2017 | Patel, Nirmal, Sellman, Collin, Lomas, Derek |
| 144 | Mining frequent learning pathways from a large educational dataset | Graph mining; Learning pathways; Process.mining; Sequence mining | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2017 | Patel, Nirmal, Sellman, Collin, Lomas, Derek |
| 144 | Mining frequent learning pathways from a large educational dataset | Graph mining; Learning pathways; Process.mining; Sequence mining | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Process.mining | No.learning.focus.outcome | 2017 | Patel, Nirmal, Sellman, Collin, Lomas, Derek |
| 144 | Mining frequent learning pathways from a large educational dataset | Graph mining; Learning pathways; Process.mining; Sequence mining | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2017 | Patel, Nirmal, Sellman, Collin, Lomas, Derek |
| 145 | Sequence modelling for analysing student interaction with educational systems | Clustering; Markov chains; Sequence modelling | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Cluster.analysis | No.learning.focus.outcome | 2017 | Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina |
| 145 | Sequence modelling for analysing student interaction with educational systems | Clustering; Markov chains; Sequence modelling | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Process.mining | No.learning.focus.outcome | 2017 | Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina |
| 145 | Sequence modelling for analysing student interaction with educational systems | Clustering; Markov chains; Sequence modelling | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2017 | Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Summative | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Summative | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-video | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Summative | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Time | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Summative | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Summative | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Summative | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Summative | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Group.event.pattern | Cluster.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Group.event.pattern | Cluster.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Group.event.pattern | Visualization.analysis | Time.on.learning | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 146 | Dynamics of MOOC Discussion Forums | MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-video | Group.event.pattern | Visualization.analysis | Collaboration | 2017 | Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Event | None | Visualization.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Event | None | Visualization.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Event | None | Basic.statistical.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Event | None | Basic.statistical.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-reading | None | Visualization.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-reading | None | Visualization.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-reading | None | Basic.statistical.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-reading | None | Basic.statistical.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-quiz | None | Visualization.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-quiz | None | Visualization.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-quiz | None | Basic.statistical.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Trace-quiz | None | Basic.statistical.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Time | None | Visualization.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Time | None | Visualization.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Time | None | Basic.statistical.analysis | Time.on.learning | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 147 | What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor | corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment | None | Method.development | other | Customized.log.data | Time | None | Basic.statistical.analysis | Learning.indicators | 2017 | Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Event | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Trace-exercise | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Trace-reading | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Trace-reading | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Trace-other | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Event | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Trace-exercise | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Trace-exercise | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Trace-reading | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Trace-reading | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Trace-other | Event.sequence | Content.analysis | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 148 | Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts | academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics | None | Method.development | other | Learning.product | Trace-other | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2017 | Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 149 | Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities | Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Visualization.analysis | Collaboration | 2017 | Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang |
| 150 | A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors | Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Jeong, Allan, Li, Haiying, Pan, Andy Jiaren |
| 150 | A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors | Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Jeong, Allan, Li, Haiying, Pan, Andy Jiaren |
| 150 | A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors | Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Jeong, Allan, Li, Haiying, Pan, Andy Jiaren |
| 150 | A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors | Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions | None | Non-srl.indicators.identification | other | Learning.product | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Jeong, Allan, Li, Haiying, Pan, Andy Jiaren |
| 150 | A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors | Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions | None | Non-srl.indicators.identification | other | Learning.product | Time | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Jeong, Allan, Li, Haiying, Pan, Andy Jiaren |
| 150 | A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors | Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions | None | Non-srl.indicators.identification | other | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Jeong, Allan, Li, Haiying, Pan, Andy Jiaren |
| 151 | Students' Careers Analysis: A Process Mining Approach | educational mining; process mining; students' career analysis | None | Method.development | None | Performance.measures | Event | Summative | Process.mining | No.learning.focus.outcome | 2017 | Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico |
| 151 | Students' Careers Analysis: A Process Mining Approach | educational mining; process mining; students' career analysis | None | Method.development | None | Performance.measures | Event | Summative | Visualization.analysis | No.learning.focus.outcome | 2017 | Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico |
| 151 | Students' Careers Analysis: A Process Mining Approach | educational mining; process mining; students' career analysis | None | Method.development | None | Performance.measures | Time | Summative | Process.mining | No.learning.focus.outcome | 2017 | Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico |
| 151 | Students' Careers Analysis: A Process Mining Approach | educational mining; process mining; students' career analysis | None | Method.development | None | Performance.measures | Time | Summative | Visualization.analysis | No.learning.focus.outcome | 2017 | Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico |
| 152 | Understanding Student Interactions in Capstone Courses to Improve Learning Experiences | capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning | None | Method.development | None | Customized.log.data | Event | Transitional.pattern | Process.mining | Course.design | 2017 | Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime |
| 152 | Understanding Student Interactions in Capstone Courses to Improve Learning Experiences | capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning | None | Method.development | None | Customized.log.data | Event | Transitional.pattern | Visualization.analysis | Course.design | 2017 | Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime |
| 152 | Understanding Student Interactions in Capstone Courses to Improve Learning Experiences | capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning | None | Method.development | None | Learner.characteristics | Event | Transitional.pattern | Process.mining | Course.design | 2017 | Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime |
| 152 | Understanding Student Interactions in Capstone Courses to Improve Learning Experiences | capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning | None | Method.development | None | Learner.characteristics | Event | Transitional.pattern | Visualization.analysis | Course.design | 2017 | Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Other.sequential.patterns | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Event | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Other.sequential.patterns | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-other | Other.sequential.patterns | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-other | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-other | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-other | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Lms.log.data | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-other | Other.sequential.patterns | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-other | Other.sequential.patterns | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-other | Other.sequential.patterns | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-other | Summative | Content.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-other | Summative | Network.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 153 | Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse | discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-other | Summative | Visualization.analysis | Collaboration | 2017 | Lee, Alwyn Vwen Yen, Tan, Seng Chee |
| 154 | Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study | Blog; Collaborative learning; Behavioral pattern; Instructional strategy | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Event | Transitional.pattern | Process.mining | Course.design | 2017 | Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi |
| 154 | Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study | Blog; Collaborative learning; Behavioral pattern; Instructional strategy | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Time | Transitional.pattern | Process.mining | Course.design | 2017 | Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi |
| 154 | Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study | Blog; Collaborative learning; Behavioral pattern; Instructional strategy | None | Non-srl.indicators.identification | collaborative knowledge building | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Course.design | 2017 | Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi |
| 154 | Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study | Blog; Collaborative learning; Behavioral pattern; Instructional strategy | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Course.design | 2017 | Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi |
| 154 | Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study | Blog; Collaborative learning; Behavioral pattern; Instructional strategy | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Time | Transitional.pattern | Process.mining | Course.design | 2017 | Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi |
| 154 | Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study | Blog; Collaborative learning; Behavioral pattern; Instructional strategy | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Course.design | 2017 | Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 155 | Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns | Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies | None | Non-srl.indicators.identification | game-based learning | Performance.measures | Trace-other | Transitional.pattern | Visualization.analysis | Learning.indicators | 2017 | Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung |
| 156 | Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving | None | None | Non-srl.indicators.identification | game-based learning | Customized.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Chen, Chih-Hung |
| 156 | Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving | None | None | Non-srl.indicators.identification | game-based learning | Customized.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Chen, Chih-Hung |
| 156 | Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving | None | None | Non-srl.indicators.identification | game-based learning | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Chen, Chih-Hung |
| 156 | Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving | None | None | Non-srl.indicators.identification | game-based learning | Performance.measures | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Hwang, Gwo-Jen, Chen, Chih-Hung |
| 157 | Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning | Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis | None | Exploring.srl.processes | SRL | Contextual | Event | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna |
| 157 | Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning | Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis | None | Exploring.srl.processes | SRL | Contextual | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna |
| 157 | Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning | Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis | None | Exploring.srl.processes | SRL | Contextual | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna |
| 157 | Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning | Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis | None | Exploring.srl.processes | SRL | Contextual | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna |
| 157 | Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning | Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis | None | Exploring.srl.processes | SRL | Contextual | Time | Transitional.pattern | Process.mining | Learning.indicators | 2017 | Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna |
| 157 | Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning | Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis | None | Exploring.srl.processes | SRL | Contextual | Time | Transitional.pattern | Process.mining | Collaboration | 2017 | Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna |
| 158 | An analysis of student collaborative problem solving activities mediated by collaborative simulations | Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli |
| 158 | An analysis of student collaborative problem solving activities mediated by collaborative simulations | Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli |
| 158 | An analysis of student collaborative problem solving activities mediated by collaborative simulations | Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation | None | Exploring.socio-dynamics | collaborative knowledge building | Customized.log.data | Trace-exercise | Transitional.pattern | Process.mining | Collaboration | 2017 | Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli |
| 158 | An analysis of student collaborative problem solving activities mediated by collaborative simulations | Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Event | Transitional.pattern | Process.mining | Collaboration | 2017 | Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli |
| 158 | An analysis of student collaborative problem solving activities mediated by collaborative simulations | Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-forum | Transitional.pattern | Process.mining | Collaboration | 2017 | Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli |
| 158 | An analysis of student collaborative problem solving activities mediated by collaborative simulations | Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation | None | Exploring.socio-dynamics | collaborative knowledge building | Learning.product | Trace-exercise | Transitional.pattern | Process.mining | Collaboration | 2017 | Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli |
| 159 | The Changing Patterns of MOOC Discourse | discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion | None | Exploring.socio-dynamics | None | Lms.log.data | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2017 | Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan |
| 159 | The Changing Patterns of MOOC Discourse | discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion | None | Exploring.socio-dynamics | None | Lms.log.data | Trace-forum | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2017 | Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan |
| 159 | The Changing Patterns of MOOC Discourse | discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion | None | Exploring.socio-dynamics | None | Lms.log.data | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2017 | Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan |
| 159 | The Changing Patterns of MOOC Discourse | discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion | None | Exploring.socio-dynamics | None | Learning.product | Event | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2017 | Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan |
| 159 | The Changing Patterns of MOOC Discourse | discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion | None | Exploring.socio-dynamics | None | Learning.product | Trace-forum | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2017 | Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan |
| 159 | The Changing Patterns of MOOC Discourse | discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion | None | Exploring.socio-dynamics | None | Learning.product | Time | Summative | Basic.statistical.analysis | No.learning.focus.outcome | 2017 | Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan |
| 160 | Modeling Student Learning Styles in MOOCs | behavior modeling; moocs; probabilistic modeling; sequential data mining | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2017 | Shi, Yuling, Peng, Zhiyong, Wang, Hongning |
| 160 | Modeling Student Learning Styles in MOOCs | behavior modeling; moocs; probabilistic modeling; sequential data mining | None | Method.development | None | Lms.log.data | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2017 | Shi, Yuling, Peng, Zhiyong, Wang, Hongning |
| 160 | Modeling Student Learning Styles in MOOCs | behavior modeling; moocs; probabilistic modeling; sequential data mining | None | Method.development | None | Learning.product | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2017 | Shi, Yuling, Peng, Zhiyong, Wang, Hongning |
| 160 | Modeling Student Learning Styles in MOOCs | behavior modeling; moocs; probabilistic modeling; sequential data mining | None | Method.development | None | Learning.product | Event | Other.sequential.patterns | Visualization.analysis | No.learning.focus.outcome | 2017 | Shi, Yuling, Peng, Zhiyong, Wang, Hongning |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Method.development | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Method.development | None | Lms.log.data | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Method.development | None | Lms.log.data | Event | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Method.development | None | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Method.development | None | Performance.measures | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Method.development | None | Performance.measures | Event | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Group.comparison | None | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Group.comparison | None | Lms.log.data | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Group.comparison | None | Lms.log.data | Event | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Group.comparison | None | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Group.comparison | None | Performance.measures | Event | Event.sequence | Cluster.analysis | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 161 | Data-driven modeling of learners' individual differences for predicting engagement and success in online learning | Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining | None | Group.comparison | None | Performance.measures | Event | Event.sequence | Other.predictions.models | No.learning.focus.outcome | 2021 | Akhuseyinoglu, Kamil, Brusilovsky, Peter |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Customized.log.data | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Customized.log.data | Event | Summative | Basic.statistical.analysis | Course.design | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Customized.log.data | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Customized.log.data | Time | Summative | Basic.statistical.analysis | Course.design | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Performance.measures | Event | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Performance.measures | Event | Summative | Basic.statistical.analysis | Course.design | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Performance.measures | Time | Summative | Basic.statistical.analysis | Time.on.learning | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 162 | Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments | Dashboard; EVE; LMS; learning; personalized; teaching | None | Method.development | None | Performance.measures | Time | Summative | Basic.statistical.analysis | Course.design | 2021 | Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Learner.characteristics | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Learner.characteristics | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Learner.characteristics | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Learner.characteristics | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Performance.measures | Trace-quiz | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Performance.measures | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 163 | Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach | Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education | None | Non-srl.indicators.identification | other | Performance.measures | Trace-feedback | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Customized.log.data | Event | Summative | Process.mining | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Customized.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Customized.log.data | Trace-exercise | Summative | Process.mining | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Customized.log.data | Trace-exercise | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Performance.measures | Event | Summative | Process.mining | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Performance.measures | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Summative | Process.mining | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 164 | Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool | Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL) | None | Exploring.srl.processes | SRL | Performance.measures | Trace-exercise | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Process.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 165 | Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data | Learning pathways; Process.mining; Self-regulated learning | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-other | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T. |
| 166 | Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis | behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) | None | Non-srl.indicators.identification | other | Contextual | Event | Transitional.pattern | Process.mining | Course.design | 2021 | Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi |
| 166 | Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis | behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) | None | Non-srl.indicators.identification | other | Contextual | Event | Transitional.pattern | Process.mining | Feedback | 2021 | Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi |
| 166 | Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis | behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) | None | Non-srl.indicators.identification | other | Contextual | Trace-forum | Transitional.pattern | Process.mining | Course.design | 2021 | Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi |
| 166 | Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis | behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) | None | Non-srl.indicators.identification | other | Contextual | Trace-forum | Transitional.pattern | Process.mining | Feedback | 2021 | Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi |
| 166 | Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis | behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) | None | Non-srl.indicators.identification | other | Contextual | Trace-feedback | Transitional.pattern | Process.mining | Course.design | 2021 | Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi |
| 166 | Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis | behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC) | None | Non-srl.indicators.identification | other | Contextual | Trace-feedback | Transitional.pattern | Process.mining | Feedback | 2021 | Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-forum | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Non-srl.indicators.identification | other | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-exercise | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-exercise | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-exercise | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-exercise | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-exercise | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-exercise | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-forum | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-forum | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-forum | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-forum | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-forum | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-forum | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-video | Event.sequence | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-video | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-video | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-video | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-video | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 167 | Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming | automated assessment; computer science; learning analytics; process mining; programming; sequence mining | None | Method.development | other | Lms.log.data | Trace-video | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga |
| 168 | Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning | computer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysis | None | Non-srl.indicators.identification | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Network.analysis | Collaboration | 2021 | Ouyang, Fan |
| 168 | Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning | computer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysis | None | Method.development | collaborative knowledge building | Learning.product | Trace-forum | Other.sequential.patterns | Network.analysis | Collaboration | 2021 | Ouyang, Fan |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Customized.log.data | Event | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Customized.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Performance.measures | Event | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Performance.measures | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Performance.measures | Trace-other | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Non-srl.indicators.identification | other | Performance.measures | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Customized.log.data | Event | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Customized.log.data | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Customized.log.data | Trace-other | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Customized.log.data | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Performance.measures | Event | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Performance.measures | Event | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Performance.measures | Trace-other | Event.sequence | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 169 | Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks | Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining | None | Method.development | other | Performance.measures | Trace-other | Summative | Basic.statistical.analysis | Learning.indicators | 2021 | He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Non-srl.indicators.identification | other | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Lms.log.data | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Event | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Event.sequence | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Event.sequence | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Transitional.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Transitional.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Transitional.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Process.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 170 | The longitudinal trajectories of online engagement over a full program | Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement | None | Method.development | other | Performance.measures | Time | Group.event.pattern | Visualization.analysis | Learning.indicators | 2021 | Saqr, Mohammed, Lopez-Pernas, Sonsoles |
| 171 | Visual search patterns, information selection strategies, and information anxiety for online information problem solving | Data science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategies | None | Non-srl.indicators.identification | other | Multimodal | Event | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tsai, Meng Jung, Wu, An Hsuan |
| 171 | Visual search patterns, information selection strategies, and information anxiety for online information problem solving | Data science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategies | None | Non-srl.indicators.identification | other | Multimodal | Trace-other | Transitional.pattern | Process.mining | Learning.indicators | 2021 | Tsai, Meng Jung, Wu, An Hsuan |
| 172 | Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining | Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior | None | Method.development | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan |
| 172 | Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining | Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior | None | Method.development | None | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan |
| 172 | Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining | Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior | None | Method.development | None | Performance.measures | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan |
| 172 | Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining | Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior | None | Method.development | None | Performance.measures | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Exploring.srl.processes | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Lms.log.data | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Event | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Event | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Event | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Event | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Trace-quiz | Event.sequence | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Trace-quiz | Event.sequence | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Frequent.sequence.mining | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 173 | Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns | Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining | None | Group.comparison | SRL | Performance.measures | Trace-quiz | Group.event.pattern | Cluster.analysis | Learning.indicators | 2021 | Zheng, Juan, Li, Shan, Lajoie, Susanne P. |
| 174 | Learner behavior prediction in a learning management system | Cognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysis | None | At-risk.student.identification | None | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2021 | Lwande, Charles, Oboko, Robert, Muchemi, Lawrence |
| 174 | Learner behavior prediction in a learning management system | Cognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysis | None | At-risk.student.identification | None | Lms.log.data | Time | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2021 | Lwande, Charles, Oboko, Robert, Muchemi, Lawrence |
| 175 | Predictive learning analytics using deep learning model in MOOCs’ courses videos | Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream | None | At-risk.student.identification | None | Lms.log.data | Event | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M. |
| 175 | Predictive learning analytics using deep learning model in MOOCs’ courses videos | Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream | None | At-risk.student.identification | None | Lms.log.data | Event | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2021 | Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M. |
| 175 | Predictive learning analytics using deep learning model in MOOCs’ courses videos | Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream | None | At-risk.student.identification | None | Lms.log.data | Time | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M. |
| 175 | Predictive learning analytics using deep learning model in MOOCs’ courses videos | Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream | None | At-risk.student.identification | None | Lms.log.data | Time | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2021 | Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M. |
| 175 | Predictive learning analytics using deep learning model in MOOCs’ courses videos | Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream | None | At-risk.student.identification | None | Lms.log.data | Trace-video | Summative | Other.predictions.models | No.learning.focus.outcome | 2021 | Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M. |
| 175 | Predictive learning analytics using deep learning model in MOOCs’ courses videos | Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream | None | At-risk.student.identification | None | Lms.log.data | Trace-video | Other.sequential.patterns | Other.predictions.models | No.learning.focus.outcome | 2021 | Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M. |
| 176 | Understanding students’ behavioural intention to use facebook as a supplementary learning platform: A mixed methods approach | Facebook; Mixed methods; Online supplementary learning platform; Perceived enjoyment; Technology acceptance | None | Non-srl.indicators.identification | affective learning | Contextual | Event | Transitional.pattern | Basic.statistical.analysis | Learning.indicators | 2021 | Hoi, Vo Ngoc, Hang, Ho Le |